Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda

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1 1 Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda Livia Alfonsi[BRAC], Oriana Bandiera[LSE] Vittorio Bassi[USC], Robin Burgess[LSE] Imran Rasul[UCL], Munshi Sulaiman[BRAC], Anna Vitali[UCL] January 2018, AEA

2 2 Motivation ² youth unemployment and underemployment are key policy challenges in the developing world ² East Africa: majority of popn aged below 25, youth represent 60% of the unemployed ² factors driving youth unemployment[freeman 1979, Becker 1994, Pissarides 1994]: { pre labor market: young workers lack skills/info { barriers to labor market entry ² this paper: labor market eld experiment to study these issues

3 3 This Paper: Two-Sided Market RCT Design ² workers: young entrants into the labor market ² rms: SMEs in eight sectors [manufacturing, services] ² twosidedexperimentaldesign:tandcworkers;tandc rms ² the RCT measures causal impacts on workers and rms of experimentally varying: { vocationally training workers before they enter the labor market[vt] { incentivising rms to hire and train workers on-the-job[ft] { matching ² shedslighton, andmatchingelementsofthelabormarket ² context: Uganda { skewed age distn, youth unemployment key policy issue

4 4 Research Questions 1: Treatment E ects on Workers ² howdotheimpactsonworkersofvtandftdi er? { contrasting supply- and demand-side policies ² outcomes: veri ed skills, employment, wages, productivity, earnings

5 5 Research Questions 2: Mechanisms ² tracked workers for four years since baseline ² whatarethesteadystateimpactsofvt/ftonworkers? ² structurally estimate a job ladder model of worker search ² key outcomes: job o er arrival rates(uj, JJ), unemployment rates, earnings ² feed into IRR calculations

6 6 Research Questions 3: Firm-Side Responses ² twosidedexperimentaldesign:tandcworkers;tandc rms ² rm side experiment allows us to measure: { whether returns to VT/FT re ect matching to di erential rms { within- rm employment displacement { pro t impacts and rent-sharing

7 8 Context: Workers ² oversubscription design used for intervention ² targeted to poorest/disadvantaged youth { notthekindsofindividualthatcanself- nancevtorft { many job training programs target youth [Card et al. 2011, Attanasio et al. 2012] ² panel data: 1714 workers tracked from baseline and three follow-ups ² [Table 1: C-group Worker Characteristics and Labor Market Outcomes]

8 Table 1: Baseline Balance on Worker Labor Market Outcomes Means, robust standard errors from OLS regressions in parentheses P-value on t-test of equality of means with control group in brackets P-value on F-tests in braces Number of workers Currently working Has worked in the last month Has done any wage employment in the last month Any self employment in the last month Has done any casual work in the last month Total earnings in the last month [USD] (1) (2) (3) (4) (5) (6) (7) All Workers (.045) (.044) (.023) (.013) (.508) (1.11)

9 9 Context: Firms ² urban labor markets throughout Uganda ² matched to nationally representative sample of 1500 SMEs { 2[1 15], =3,operatingineightsectors: { welding, motor mechanics, construction,..,hairdressing ² wage subsidy covering rm's screening/learning costs { employer learning [Farber and Gibbons 1996, Altonji and Pierret 2001] { apprenticeships as screening technologies [Autor 2001, Hardy and McCasland 2015] ² [Table 2: Vocational Training] ² [Table 3: On-the-Job Training]

10 Table 2: The Mincerian Returns to Vocational Training Worker is skilled: self-reported VTI attendance % workers skilled in sector Coefficient and SE from worker wage regressions [USD] Coefficient and SE from worker log(wage) regressions [USD] All Sectors 31.0% 26.2***.515*** (3.15) (.045) Manufacturing Welding 24.9% 34.5***.381*** (6.40) (.084) Motor-mechanics 23.5% 16.1*.294* (9.41) (.153) Electrical wiring 41.9% 27.3***.486** (7.60) (.189) Construction 28.8% * (9.39) (.170) Plumbing 49.1% 60.9***.719** (19.0) (.281) Services Hairdressing 29.2% 22.9***.444*** (5.97) (.069) Tailoring 41.6% *** (9.76) (.182) Catering 40.2% 26.8**.330*** (11.6) (.109)

11 Table 3: Characteristics of Apprenticeships A. Availability Worker received on-the-job training at the current firm.498 Duration of on-the-job training [months] 10 B. Payments In the first month of training, the worker: Was paid.198 Was unpaid.515 Was paying the firm owner.288 Earnings (conditional on > 0) [US$] (median) 39.2 (40.1) Amount worker was paying to owner (conditional on > 0) [US$] (median) 51.9 (33.3) C. Trainers Who was mainly involved in training the worker: Firm owner only.457 Other employees only.091 Firm owner as well as other employees.452

12 Training Figure 1: Experimental Design A. Worker Side Design T3: Vocationally Trained (390 workers) T4: Vocationally Trained + Matched (307 workers) 1714 Workers T5: Untrained, Matched (283 workers) No training T2: Firm-trained (wage subsidy + matched) (283 workers) T1: Control (451 workers)

13 12 Firm Training[T2] ² rm paid 120K UGX/month = $50 (for 6 months) to hire an untrained worker ² in exiblewagesubsidywithdesignatedsplit:$12 5toowner,$38toworker ² twoanchorsforthissplit: ² forthosereportingtobeanapprenticewithawage,meanwageis$39 ² wages of all unskilled workers { subsidy rate for unskilled workers (subsidy/average wage): 63% [de Mel et al. 2010, SR=50%]

14 14 Certi cation and Skills Composition ² certi cation: { VT workers can signal their skills to employers { value of certi cation [Pallais 2014, MacLeod et al. 2016, Bassi and Nansamba 2017] { SMevidence:UJandJJtransitions ² incentives for rms to train workers depends on labor market imperfections [Acemoglu and Pischke 1998, 1999] ² VTworkersmorelikelytobepoachedthanFT!tiltstobalancetowards latter having relatively more rm speci c skills { RF evidence: skills, wages, productivity, rm pro ts { SMevidence:UJandJJtransitions

15 18 Estimation ² observeworker intreatmentgroup instrata insurveywave = ² estimatethefollowingancovaspeci cationinsurveywaves =1 2 3: = X x ² worker 'sassignedtreatment ( treatments) ² : strataandsurveywave xede ects ² randomizationatworkerlevel( ):robuststandarderrors

16 21 Skills: Task Composition, Sector- and Firm-speci c Skills ² rm-sponsored training ² conductedsectoral-speci cskillstestonworkers(incl. ),administeredat second and third follow-up ² try to measure rm-speci c skills(third follow up) ² sectoral task lists by training type(o*net) ² [Table 4: Skills] ² [Figure 3: Tasks] ² [Table 5: Employment(Extensive and Total Margins)] ² [Figure 4: Productivity Bounds]

17 Table 4: Skills OLS regression coefficients, IPW estimates, robust standard errors in parentheses Received OTJ-T at First Employer Position in First Job is "Trainee" (1) (2) Firm Trained.144***.215*** (.052) (.041) Vocationally Trained (.042) (.025) Mean (SD) Outcome in Control Group Control for Baseline Value No No P-values on tests of equality: Firm-Provided Training Firm Trained = Vocationally Trained [.000] [.000] N. of observations

18 Table A5: Sector Skills Test for Motor Mechanics

19 Table 4: Skills OLS regression coefficients, IPW estimates, robust standard errors in parentheses Report Some Skills ITT LATE: Offered Worker-Firm Match Firm-Specific Skills Skills Transferability (3) (4) (5) (6) Firm Trained.110*** (.032) (1.53) (3.00) (.109) Vocationally Trained.269*** 6.96*** 56.8***.150** (.023) (1.20) (15.5) (.068) Mean (SD) Outcome in Control Group (22.9) 30.1 (22.9) - Control for Baseline Value No No No No P-values on tests of equality: Sector-Specific Skills Test Firm Trained = Vocationally Trained [.000] [.001] [.000] [.025] N. of observations 1,818 1,818 1,

20 Figure 3: Tasks Performed by Vocationally Trained and Firm Trained Workers Panel A: Manufacturing 60% %VT - %FT 40% 20% 0% -20% -40% Dig a foundation Fit a sink Fit a sink on a slab Fitting a bath tub Connecting water pipes Spraying Fetching water Repairing wornout tyres Making metallic roofs Plaking plate stands Making a double bed Making designs on doors Making restaurant chairs Making a door 3*5 Making a single door -60% -80% ALL TASKS

21 Table 5: Employment and Earnings OLS regression coefficients, IPW estimates, robust standard errors in parentheses Has done any work in the last month Number of months worked in the last year Hourly wage rate [USD] Total earnings in the last month [USD] (1) (2) (3) (4) Firm Trained.063**.518**.028** 5.80** (.025) (.259) (.012) (2.53) Vocationally Trained.090***.879***.031*** 9.75*** (.020) (.207) (.009) (2.01) Mean Outcome in Control Group Control for Baseline Value Yes No Yes Yes P-values on tests of equality: Firm Trained = Vocationally Trained [.255] [.134] [.799] [.111] N. of observations 3,256 3,256 3,099 3,111

22 22 Underlying Market Failure ² with such high returns from training, why do workers not self-invest in their HK? ² credit constraints likely bind in this sample { total cost: $470 per trainee split as VTI ($400) + out-of-pocket costs ($70) ² credit constraints also prevent workers paying for FT ² credit constraints on rms prevent them paying up front hiring/screening costs of employing youth ² worker beliefs: { imperfect information about returns to skills in low-income labor markets [Jensen 2009, Kaufmann 2014] ² [Table A6: Worker Beliefs]

23 Table A6: Worker Expectations Means, standard deviations in parenthesis All amounts in 2012 USD Expected probability of finding a job in the next 12 months Average expected monthly earnings (triangular distribution) With current Skill Set If received VT With current Skill Set If received VT (1) (2) (3) (4) All Workers (Baseline Interview) (.288) (.144) (46.9) (71.5) N. of observations 1,611 1,589 1,243 1,411

24 26 Value Functions ² value function for an unemployed worker is: " 0 ( )maxf R ( ) ( j ) ( )g ( )= ( )+ +(1 0 ( )) ( ) # ² valuefunctionforanemployedworkerwithwage is: " ( )+ 1 ( )maxf R ( ) ( j ) ( )g ( )= ( )+ +(1 1 ( ) ( ) #

25 28 Treatments and Job Search ² training can a ect worker behavior through two mechanisms: { theprobabilitiesofreceivingajobo er:( 0 ( ), 1 ( )) { thedistributionofo eredwages( ( j )) ² through these mechanisms training impacts endogenous choices: { searche ort( ) { whether to accept or reject wage o ers(reservation wage) ² [Table 6: Worker Beliefs and Search]

26 Table 6: Worker Beliefs and Job Search OLS regression coefficients, IPW estimates, robust standard errors in parentheses Job Offer Probability Offered Wages Search Intensity Expected probability of finding a job in the next 6 months (0 to 10 scale) Average expected monthly earnings (triangular distribution) [USD] Has actively looked for a job in the last year (1) (4) (5) Firm Trained.593*** (.137) (3.34) (.025) Vocationally Trained 1.87*** 19.4***.093*** (.108) (2.75) (.020) Mean Outcome in Control Group Control for Baseline Value Yes Yes No P-values on tests of equality: Firm Trained = Vocationally Trained [.000] [.000] [.000] N. of observations 3,136 1,905 3,255

27 29 Estimation ² follow two-step procedure in Bontemps et al. [2000] { 0 1, areestimated,asymptoticse'scalculated ² [Table7,PanelA:JobDestructionandJobO errates] ² [Table7,PanelA: 1 ] ² [Table7,PanelB: ( )and ( )Estimates]

28 Table 7: Estimates of the Job Ladder Search Model Two-step estimation procedure in Bontemps, Robin and van den Berg [2000] Panel A: Parameter Estimates Control Firm Trained δ Vocationally Trained (1) (2) (3) Job destruction rate (monthly): (.0030) (.0037) (.0021) Arrival rate of job offers if UNEMPLOYED (monthly): (.0019) (.0024) (.0019) Arrival rate of job offers if EMPLOYED (monthly): (.0096) (.0117) (.0080) Interfirm competition for workers u Steady State: November 2015 (Data from Second and Third Follow-up) Unemployment Rate % Impact: 2.3% 14.7%

29 Table 7: Estimates of the Job Ladder Search Model Two-step estimation procedure in Bontemps, Robin and van den Berg [2000] Panel B: Function and Income Estimates Steady State: November 2015 (Data from Second and Third Follow-up) Average (sd) monthly OFFERED wage [USD] F(.) (37.4) (43.6) (41.9) Average (sd) monthly ACCEPTED wage [USD] G(.) (45.5) (54.5) (54.4) Treatment Effect Impact on Annual Income [USD] % Impact: 11.8% 34.3%

30 35 IRR ² IRR challenge versus capital/cash transfers in low-income settings [Blattman and Ralston 2015] ² VTcost: $470pertraineesplitasVTI($400)+out-of-pocketcosts($70) ² FTcost: $50 3x6months=$302pertrainee ² SS earnings impact 3 times larger for vocational training: $107 versus $37 ² opportunity costs: foregone earnings while being trained ² [Table10: IRR] ² [Figure 7: McKenzie 2017 Meta-analysis]

31 Table 10: Internal Rate of Return Firm Trained Vocationally Trained (1) (2) Social discount rate = 5% Remaining expected productive life of beneficiaries 38 years 38 years Panel A. External parameters Total cost per individual at year 0 [USD]: (i) Training costs (for 6 months) (ii) Program overheads costs 31 4 (iii) Foregone earnings (for 6 months) - average at baseline Panel B. Estimated total earnings benefits 2 Benefits/cost ratio Social discount rate = 10% Internal Rate of Return (IRR) Panel C. Sensitivity Sensitivity to different expected remaining productive life of beneficiaries Remaining expected productive life = 10 years Panel D. Programme Costs for IRR to equate social discount rate 5 Total cost per individual at year 0 [USD]

32 Figure 7: Comparison of Treatment Impacts to Meta-analysis by McKenzie [2017] Panel A: Employment impacts Panel B: Earnings impacts Red: Job search assistance/matching Green: Wage subsidies Blue: Vocational training T3: VT T2: FT Red: Job search assistance/matching Green: Wage subsidies Blue: Vocational training T2: FT T3: VT

33 36 External Validity ² we have documented large impacts of training relative to studies in middleand high-income countries: why? ² our e ect sizes are large relative to literature ² rankingoftreatmentssimilartoearlierstudies(vt match) { sectoral focus: reduced mismatch { worker selection into evaluation sample, low attrition { treatment intensity { VTI quality (interacting with imperfect information of workers)

34 37 Motivation ² youth unemployment and underemployment are key policy challenges in the developing world ² factors driving youth unemployment: { pre labor market: young workers lack skills/info { barriers to labor market entry ² this paper: labor market eld experiment to study these issues { both workers and rms constrained { returnstovtdominateft { role of VTI sector and certi cation

Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda

Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda Livia Alfonsi Oriana Bandiera Vittorio Bassi Robin Burgess Imran Rasul Munshi Sulaiman Anna Vitali y September 2017 Abstract